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Julia: split symbolic-node.jl into several snippets (#14024)
- `symbolic-node/type.jl` - `symbolic-node/show.jl` - `symbolic-node/arithmetic.jl` - `symbolic-node/io.jl` - `symbolic-node/array.jl` - `symbolic-node/op.jl` - `symbolic-node/autodiff.jl` See also: #14001
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‎julia/src/symbolic-node.jl‎

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‎julia/src/symbolic-node/arithmetic.jl‎

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# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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import Base: +
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"""
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+(args...)
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.+(args...)
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Elementwise summation of `SymbolicNode`.
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"""
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function +(x::SymbolicNode, ys::SymbolicNodeOrReal...)
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ret = x
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for y ∈ ys
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if y isa SymbolicNode
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ret = _plus(ret, y)
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else
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ret = _plus_scalar(ret, scalar=MX_float(y))
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end
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end
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ret
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end
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+(s::Real, x::SymbolicNode, ys::SymbolicNodeOrReal...) = +(x + s, ys...)
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broadcasted(::typeof(+), x::SymbolicNode, ys::SymbolicNodeOrReal...) = +(x, ys...)
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broadcasted(::typeof(+), s::Real, x::SymbolicNode, ys::SymbolicNodeOrReal...) = +(x + s, ys...)
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import Base: -
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"""
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-(x, y)
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.-(x, y)
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Elementwise substraction of `SymbolicNode`.
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Operating with `Real` is available.
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"""
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x::SymbolicNode - y::SymbolicNode = _minus(x, y)
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x::SymbolicNode - s::Real = _minus_scalar(x, scalar=MX_float(s))
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s::Real - x::SymbolicNode = _rminus_scalar(x, scalar=MX_float(s))
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-(x::SymbolicNode) = 0 - x
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broadcasted(::typeof(-), x::SymbolicNode, y::SymbolicNodeOrReal) = x - y
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broadcasted(::typeof(-), s::Real, x::SymbolicNode) = s - x
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import Base: *
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"""
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.*(x, y)
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Elementwise multiplication of `SymbolicNode`.
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"""
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x::SymbolicNode * s::Real = _mul_scalar(x, scalar=MX_float(s))
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s::Real * x::SymbolicNode = _mul_scalar(x, scalar=MX_float(s))
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function broadcasted(::typeof(*), x::SymbolicNode, ys::SymbolicNodeOrReal...)
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ret = x
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for y in ys
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if y isa SymbolicNode
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ret = _mul(ret, y)
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else
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ret = _mul_scalar(ret, scalar=MX_float(y))
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end
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end
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ret
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end
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broadcasted(::typeof(*), s::Real, x::SymbolicNode, ys::SymbolicNodeOrReal...) =
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broadcasted(*, x * s, ys...)
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import Base: /
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"""
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./(x, y)
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* Elementwise dividing a `SymbolicNode` by a scalar or another `SymbolicNode`
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of the same shape.
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* Elementwise divide a scalar by an `SymbolicNode`.
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* Matrix division (solving linear systems) is not implemented yet.
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"""
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x::SymbolicNode / s::Real = _DivScalar(x, scalar=MX_float(s))
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broadcasted(::typeof(/), x::SymbolicNode, y::SymbolicNode) = _div(x, y)
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broadcasted(::typeof(/), x::SymbolicNode, s::Real) = _div_scalar(x, scalar=MX_float(s))
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broadcasted(::typeof(/), s::Real, x::SymbolicNode) = _rdiv_scalar(x, scalar=MX_float(s))
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import Base: ^
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"""
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.^(x, y)
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Elementwise power of `SymbolicNode` and `NDArray`.
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Operating with `Real` is available.
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"""
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^
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broadcasted(::typeof(^), x::SymbolicNode, y::SymbolicNode) = _power(x, y)
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broadcasted(::typeof(^), x::SymbolicNode, s::Real) = _power_scalar(x, scalar = s)
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broadcasted(::typeof(^), s::Real, x::SymbolicNode) = _rpower_scalar(x, scalar = s)
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broadcasted(::typeof(Base.literal_pow), ::typeof(^), x::SymbolicNode, ::Val{s}) where {s} =
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_power_scalar(x, scalar = s)
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broadcasted(::typeof(^), ::Irrational{:ℯ}, x::SymbolicNode) = exp(x)
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broadcasted(::typeof(^), x::SymbolicNode, s::Irrational) =
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_power_scalar(x, scalar=MX_float(s))
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broadcasted(::typeof(^), s::Irrational, x::SymbolicNode) =
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_rpower_scalar(x, scalar=MX_float(s))
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‎julia/src/symbolic-node/array.jl‎

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# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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# Base.Array related interface
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import Base: reshape
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"""
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reshape(sym::SymbolicNode, dim; reverse=false, name)
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reshape(sym::SymbolicNode, dim...; reverse=false, name)
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Reshape SymbolicNode operator
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Some dimensions of the shape can take special values from the set
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{0, -1, -2, -3, -4}.
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The significance of each is explained below:
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- `0` copy this dimension from the input to the output shape.
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Example:
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- input shape = (2,3,4), shape = (4,0,2), output shape = (4,3,2)
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- input shape = (2,3,4), shape = (2,0,0), output shape = (2,3,4)
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- `-1` infers the dimension of the output shape by using the remainder of the
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input dimensions keeping the size of the new array same as that of the input
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array. At most one dimension of shape can be -1.
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Example:
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- input shape = (2,3,4), shape = (6,1,-1), output shape = (6,1,4)
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- input shape = (2,3,4), shape = (3,-1,8), output shape = (3,1,8)
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- input shape = (2,3,4), shape=(-1,), output shape = (24,)
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- `-2` copy all/remainder of the input dimensions to the output shape.
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Example:
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- input shape = (2,3,4), shape = (-2,), output shape = (2,3,4)
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- input shape = (2,3,4), shape = (2,-2), output shape = (2,3,4)
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- input shape = (2,3,4), shape = (-2,1,1), output shape = (2,3,4,1,1)
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- `-3` use the product of two consecutive dimensions of the input shape as the
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output dimension.
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Example:
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- input shape = (2,3,4), shape = (-3,4), output shape = (6,4)
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- input shape = (2,3,4,5), shape = (-3,-3), output shape = (6,20)
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- input shape = (2,3,4), shape = (0,-3), output shape = (2,12)
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- input shape = (2,3,4), shape = (-3,-2), output shape = (6,4)
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- `-4` split one dimension of the input into two dimensions passed subsequent
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to -4 in shape (can contain -1).
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Example:
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- input shape = (2,3,4), shape = (-4,1,2,-2), output shape = (1,2,3,4)
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- input shape = (2,3,4), shape = (2,-4,-1,3,-2), output shape = (2,1,3,4)
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If the argument `reverse` is set to `1`, then the special values are inferred
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from right to left.
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Example:
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- with `reverse=false`, for input shape = (10,5,4), shape = (-1,0),
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output shape would be (40,5)
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- with `reverse=true`, output shape will be (50,4).
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"""
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reshape(sym::SymbolicNode, dim::NTuple{N, Integer}; kwargs...) where {N} =
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_reshape(sym, dim; kwargs...)
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reshape(sym::SymbolicNode, dim::Integer...; kwargs...) =
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_reshape(sym, dim; kwargs...)
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@inline function _reshape(sym::SymbolicNode, dim::NTuple{N,Integer};
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reverse::Bool=false, name::String="") where N
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op = _get_cached_libmx_op_handle("reshape")
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node = _create_atomic_symbol(op.value, ["shape", "reverse"],
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[dump_mx_param(dim), dump_mx_param(!reverse)])
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name = get!(DEFAULT_NAME_MANAGER, name, "reshape")
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_compose!(node, name=name, data=sym)
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end
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################################################################################
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# Base.getindex
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################################################################################
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"""
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getindex(self :: SymbolicNode, idx :: Union{Int, Base.Symbol, AbstractString})
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Get a node representing the specified output of this node. The index could be
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a symbol or string indicating the name of the output, or a 1-based integer
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indicating the index, as in the list of [`list_outputs`](@ref).
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"""
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function Base.getindex(self :: SymbolicNode, idx :: Union{Base.Symbol, AbstractString})
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idx = Symbol(idx)
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i_idx = findall(idx .== list_outputs(self))
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@assert(length(i_idx) > 0, "Cannot find output with name '$idx'")
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@assert(length(i_idx) < 2, "Found duplicated output with name '$idx'")
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Base.getindex(self, i_idx[1])
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end
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function Base.getindex(self :: SymbolicNode, idx :: Int)
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ref_hdr = Ref{MX_handle}(0)
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# note Julia is 1-based, while MXNet is 0-based
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@mxcall(:MXSymbolGetOutput, (MX_handle, MX_uint, Ref{MX_handle}), self, idx-1, ref_hdr)
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return SymbolicNode(MX_SymbolHandle(ref_hdr[]))
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end
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