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annotate LazyTensors/src/lazy_operations.jl @ 196:b3c252280a19 boundary_conditions

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Move LazyArray and make LazyTensorMappingApplication and LazyElementwiseOperation subtypes of it
author Jonatan Werpers <jonatan@werpers.com>
date Thu, 20 Jun 2019 23:25:38 +0200
parents 5413067d2c4a
children a340fa91b1fc
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1 """
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2 LazyTensorMappingTranspose{T,R,D} <: TensorMapping{T,D,R}
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3
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4 Struct for lazy transpose of a TensorMapping.
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5
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6 If a mapping implements the the `apply_transpose` method this allows working with
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7 the transpose of mapping `m` by using `m'`. `m'` will work as a regular TensorMapping lazily calling
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8 the appropriate methods of `m`.
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9 """
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10 struct LazyTensorMappingTranspose{T,R,D} <: TensorMapping{T,D,R}
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11 tm::TensorMapping{T,R,D}
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12 end
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13 export LazyTensorMappingTranspose
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14
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15 # # TBD: Should this be implemented on a type by type basis or through a trait to provide earlier errors?
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16 Base.adjoint(t::TensorMapping) = LazyTensorMappingTranspose(t)
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17 Base.adjoint(t::LazyTensorMappingTranspose) = t.tm
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18
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19 apply(tm::LazyTensorMappingTranspose{T,R,D}, v::AbstractArray{T,R}, I::Vararg) where {T,R,D} = apply_transpose(tm.tm, v, I...)
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20 apply_transpose(tm::LazyTensorMappingTranspose{T,R,D}, v::AbstractArray{T,D}, I::Vararg) where {T,R,D} = apply(tm.tm, v, I...)
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21
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22 range_size(tmt::LazyTensorMappingTranspose{T,R,D}, d_size::NTuple{R,Integer}) where {T,R,D} = domain_size(tmt.tm, domain_size)
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23 domain_size(tmt::LazyTensorMappingTranspose{T,R,D}, r_size::NTuple{D,Integer}) where {T,R,D} = range_size(tmt.tm, range_size)
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27 """
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28 LazyArray{T,D} <: AbstractArray{T,D}
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29
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30 Array which is calcualted lazily when indexing.
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31
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32 A subtype of `LazyArray` will use lazy version of `+`, `-`, `*`, `/`.
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33 """
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34 abstract type LazyArray{T,D} <: AbstractArray{T,D} end
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35 export LazyArray
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36
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37 """
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38 LazyTensorMappingApplication{T,R,D} <: LazyArray{T,R}
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39
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40 Struct for lazy application of a TensorMapping. Created using `*`.
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41
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42 Allows the result of a `TensorMapping` applied to a vector to be treated as an `AbstractArray`.
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43 With a mapping `m` and a vector `v` the LazyTensorMappingApplication object can be created by `m*v`.
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44 The actual result will be calcualted when indexing into `m*v`.
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45 """
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46 struct LazyTensorMappingApplication{T,R,D} <: LazyArray{T,R}
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47 t::TensorMapping{T,R,D}
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48 o::AbstractArray{T,D}
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49 end
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50 export LazyTensorMappingApplication
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51
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52 Base.:*(tm::TensorMapping{T,R,D}, o::AbstractArray{T,D}) where {T,R,D} = LazyTensorMappingApplication(tm,o)
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53
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54 Base.getindex(ta::LazyTensorMappingApplication{T,R,D}, I::Vararg) where {T,R,D} = apply(ta.t, ta.o, I...)
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55 Base.size(ta::LazyTensorMappingApplication{T,R,D}) where {T,R,D} = range_size(ta.t,size(ta.o))
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56 # TODO: What else is needed to implement the AbstractArray interface?
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57
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59 # # We need the associativity to be a→b→c = a→(b→c), which is the case for '→'
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60 Base.:*(args::Union{TensorMapping{T}, AbstractArray{T}}...) where T = foldr(*,args)
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61 # # Should we overload some other infix binary operator?
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62 # →(tm::TensorMapping{T,R,D}, o::AbstractArray{T,D}) where {T,R,D} = LazyTensorMappingApplication(tm,o)
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63 # TODO: We need to be really careful about good error messages.
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64 # For example what happens if you try to multiply LazyTensorMappingApplication with a TensorMapping(wrong order)?
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67
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68 """
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69 LazyElementwiseOperation{T,D,Op, T1<:AbstractArray{T,D}, T2 <: AbstractArray{T,D}} <: AbstractArray{T,D}
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70
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71 Struct allowing for lazy evaluation of elementwise operations on AbstractArrays.
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72
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73 A LazyElementwiseOperation contains two AbstractArrays of equal size,
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74 together with an operation. The operations are carried out when the
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75 LazyElementwiseOperation is indexed.
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76 """
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77 struct LazyElementwiseOperation{T,D,Op, T1<:AbstractArray{T,D}, T2 <: AbstractArray{T,D}} <: LazyArray{T,D}
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78 a::T1
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79 b::T2
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80
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81 function LazyElementwiseOperation{T,D,Op}(a::T1,b::T2) where {T,D,Op, T1<:AbstractArray{T,D}, T2<:AbstractArray{T,D}}
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82 return new{T,D,Op,T1,T2}(a,b)
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83 end
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84 end
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85
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86 Base.size(v::LazyElementwiseOperation) = size(v.a)
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87
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88 # TODO: Make sure boundschecking is done properly and that the lenght of the vectors are equal
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89 # NOTE: Boundschecking in getindex functions now assumes that the size of the
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90 # vectors in the LazyElementwiseOperation are the same size. If we remove the
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91 # size assertion in the constructor we might have to handle
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92 # boundschecking differently.
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93 Base.@propagate_inbounds @inline function Base.getindex(leo::LazyElementwiseOperation{T,D,:+}, I...) where {T,D}
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94 @boundscheck if !checkbounds(Bool,leo.a,I...)
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95 throw(BoundsError([leo],[I...]))
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96 end
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97 return leo.a[I...] + leo.b[I...]
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98 end
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99 Base.@propagate_inbounds @inline function Base.getindex(leo::LazyElementwiseOperation{T,D,:-}, I...) where {T,D}
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100 @boundscheck if !checkbounds(Bool,leo.a,I...)
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101 throw(BoundsError([leo],[I...]))
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102 end
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103 return leo.a[I...] - leo.b[I...]
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104 end
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105 Base.@propagate_inbounds @inline function Base.getindex(leo::LazyElementwiseOperation{T,D,:*}, I...) where {T,D}
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106 @boundscheck if !checkbounds(Bool,leo.a,I...)
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107 throw(BoundsError([leo],[I...]))
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108 end
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109 return leo.a[I...] * leo.b[I...]
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110 end
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111 Base.@propagate_inbounds @inline function Base.getindex(leo::LazyElementwiseOperation{T,D,:/}, I...) where {T,D}
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112 @boundscheck if !checkbounds(Bool,leo.a,I...)
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113 throw(BoundsError([leo],[I...]))
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114 end
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115 return leo.a[I...] / leo.b[I...]
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116 end
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117
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118 # Define lazy operations for AbstractArrays. Operations constructs a LazyElementwiseOperation which
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119 # can later be indexed into. Lazy operations are denoted by the usual operator followed by a tilde
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120 @inline +̃(a::AbstractArray{T,D},b::AbstractArray{T,D}) where {T,D} = LazyElementwiseOperation{T,D,:+}(a,b)
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121 @inline -̃(a::AbstractArray{T,D},b::AbstractArray{T,D}) where {T,D} = LazyElementwiseOperation{T,D,:-}(a,b)
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122 @inline *̃(a::AbstractArray{T,D},b::AbstractArray{T,D}) where {T,D} = LazyElementwiseOperation{T,D,:*}(a,b)
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123 @inline /̃(a::AbstractArray{T,D},b::AbstractArray{T,D}) where {T,D} = LazyElementwiseOperation{T,D,:/}(a,b)
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124
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125
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126 Base.:+(a::LazyArray{T,D},b::AbstractArray{T,D}) where {T,D} = a +̃ b
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127 Base.:+(a::AbstractArray{T,D}, b::LazyArray{T,D}) where {T,D} = b + a
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128 Base.:-(a::LazyArray{T,D},b::AbstractArray{T,D}) where {T,D} = a -̃ b
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129 Base.:-(a::AbstractArray{T,D}, b::LazyArray{T,D}) where {T,D} = a -̃ b
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130 Base.:*(a::LazyArray{T,D},b::AbstractArray{T,D}) where {T,D} = a *̃ b
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131 Base.:*(a::AbstractArray{T,D},b::LazyArray{T,D}) where {T,D} = b * a
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132 # TODO: / seems to be ambiguous
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133 # Base.:/(a::LazyArray{T,D},b::AbstractArray{T,D}) where {T,D} = a /̃ b
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134 # Base.:/(a::AbstractArray{T,D},b::LazyArray{T,D}) where {T,D} = a /̃ b
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135
195
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136 export +̃, -̃, *̃, /̃
194
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137
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138
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139
190
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140 # TODO: Write tests and documentation for LazyTensorMappingComposition
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141 # struct LazyTensorMappingComposition{T,R,K,D} <: TensorMapping{T,R,D}
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142 # t1::TensorMapping{T,R,K}
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143 # t2::TensorMapping{T,K,D}
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144 # end
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145
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146 # Base.:∘(s::TensorMapping{T,R,K}, t::TensorMapping{T,K,D}) where {T,R,K,D} = LazyTensorMappingComposition(s,t)
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147
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148 # function range_size(tm::LazyTensorMappingComposition{T,R,K,D}, domain_size::NTuple{D,Integer}) where {T,R,K,D}
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149 # range_size(tm.t1, domain_size(tm.t2, domain_size))
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150 # end
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151
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152 # function domain_size(tm::LazyTensorMappingComposition{T,R,K,D}, range_size::NTuple{R,Integer}) where {T,R,K,D}
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153 # domain_size(tm.t1, domain_size(tm.t2, range_size))
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154 # end
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155
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156 # function apply(c::LazyTensorMappingComposition{T,R,K,D}, v::AbstractArray{T,D}, I::Vararg) where {T,R,K,D}
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157 # apply(c.t1, LazyTensorMappingApplication(c.t2,v), I...)
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158 # end
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159
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160 # function apply_transpose(c::LazyTensorMappingComposition{T,R,K,D}, v::AbstractArray{T,D}, I::Vararg) where {T,R,K,D}
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161 # apply_transpose(c.t2, LazyTensorMappingApplication(c.t1',v), I...)
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162 # end
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163
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164 # # Have i gone too crazy with the type parameters? Maybe they aren't all needed?
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165
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166 # export →